Abstract
The data mining task of online unsupervised learning of streaming data continually arriving at the system in complex dynamic environments under conditions of uncertainty is an NP-hard optimization problem for general metric spaces and is computationally intractable for real-world problems of practical interest. The primary contribution of this work is a multi-agent method for continuous agglomerative hierarchical clustering of streaming data, and a knowledge-based selforganizing competitive multi-agent system for implementing it. The reported experimental results demonstrate the applicability and efficiency of the implemented adaptive multi-agent learning system for continuous online clustering of both synthetic datasets and datasets from the following real-world domains: the RoboCup Soccer competition, and gene expression datasets from a bioinformatics test bed.
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References
Aggarwal, C.C. (ed.): Data Streams: Models and Algorithms. Advances in Database Systems. Springer, New York, NY, USA (2007)
Al-Shalalfa, M., Alhajj, R.: Application of double clustering to gene expression data for class prediction. In: AINA Workshops (1), pp. 733–738. IEEE Computer Society (2007)
Auroop R. Ganguly Joao Gama, O.A.O.M.M.G.R.R.V. (ed.): Knowledge Discovery from Sensor Data. CRC Press Inc - Taylor and Francis Ltd, New York, NY, USA (2008)
Bagherjeiran, A., Eick, C.F., Chen, C.S., Vilalta, R.: Adaptive clustering: Obtaining better clusters using feedback and past experience. IEEE International Conference on Data Mining 0, 565–568 (2005)
Davidson, I., Ravi, S.S.: Agglomerative hierarchical clustering with constraints: Theoretical and empirical results. In: PKDD-05, LNCS, vol. 3721. Springer (2005)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv. 31(3), 264–323 (1999)
Kiselev, I., Alhajj, R.: An adaptive multi-agent system for continuous learning of streaming data. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT’08) 2, 148–153 (2008)
Kiselev, I., Alhajj, R.: Online dynamic optimization under conditions of uncertainty. In: A.P.S.D.R. Nicholas R. Jennings Alex Rogers (ed.) 1th International Workshop “Optimisation in Multi-Agent Systems” (OptMAS), pp. 52–59. AAMAS-08 (2008)
Kiselev, I., Alhajj, R.: Supplementary materials, software demonstration, and video recordings of the developed multi-agent learning system, described in this work. Website (2008). \footnotesize{\ttfamily{http://www.multiagent.org/mining}}
Kiselev, I., Glaschenko, A., Chevelev, A., Skobelev, P.: Towards an adaptive approach for distributed resource allocation in a multi-agent system for solving dynamic vehicle routing problems. In: AAAI-07, pp. 1874–1875 (2007)
Klusch, M., Lodi, S., Moro, G.: Issues of agent-based distributed data mining. In: AAMAS-03, pp. 1034–1035 (2003)
Likas, A.: A reinforcement learning approach to online clustering. Neural Computation 11(8), 1915–1932 (1999)
MacKie-Mason, J.K., Wellman, M.P.: Handbook of Computational Economics, vol. Volume 2, chap. Chapter 28 Automated Markets and Trading Agents, pp. 1381–1431. Elsevier (2006)
Modi, P.J., Jung, H., Tambe, M., Shen, W.M., Kulkarni, S.: Dynamic distributed resource allocation: A distributed constraint satisfaction approach. In: ATAL: Revised Papers from the 8th Intern. Workshop on Intelligent Agents VIII, pp. 264–276. Springer-Verlag, UK (2002)
Modi, P.J., Shen, W.M., Tambe, M., Yokoo, M.: Adopt: asynchronous distributed constraint optimization with quality guarantees. Artif. Intell. 161(1–2), 149–180 (2005)
Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V. (eds.): Algorithmic Game Theory. Cambridge University Press, New York, NY, USA (2007)
Rodrigues, P.P., Gama, J., Pedroso, J.P.: Hierarchical clustering of time-series data streams. IEEE Trans. Knowl. Data Eng. 20(5), 615–627 (2008)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd international ed. edn. Prentice-Hall, Upper Saddle River, NJ, USA (2003)
Smith, R.G.: The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Trans. Computers 29(12), 1104–1113 (1980)
Symeonidis, A.L., Mitkas, P.A.: Agent Intelligence Through Data Mining. Multiagent Systems, Artificial Societies, and Simulated Organizations. Springer-Verlag New York (2005)
Theocharopoulou, C., Partsakoulakis, I., Vouros, G.A., Stergiou, K.: Overlay networks for task allocation and coordination in dynamic large-scale networks of cooperative agents. In: E.H. Durfee, M. Yokoo, M.N. Huhns, O. Shehory (eds.) AAMAS, p. 55. IFAAMAS
Zhang, S., Zhang, C., Wu, X.: Knowledge Discovery in Multiple Databases. Springer-Verlag (2004)
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Kiselev, I., Alhajj, R. (2009). A Multiagent Approach to Adaptive Continuous Analysis of Streaming Data in Complex Uncertain Environments. In: Cao, L. (eds) Data Mining and Multi-agent Integration. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0522-2_14
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DOI: https://doi.org/10.1007/978-1-4419-0522-2_14
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